Vancouver Trees Dataset Analysis and Visualization Report

Oct 22, 2022

Notebook by Renbo Xu

Introduction

My nationality is China. I came to Canada to study several years ago and what impressed me the most is its beautiful natural environment. With its scenic views, mild climate, and friendly people, Vancouver is well-known around the world as both a popular tourist attraction and one of the best places to live (https://vancouver.ca/news-calendar/our-city.aspx). Its diverse natual environment,such as mountains, oceans and diverse wild animal, has attarcted a lot of immigrants to live here. One of the most important features, vancouver trees, also contributes a lot to the amazing beauty of Vancouver city. I am curious about how trees are distributed in different vancouver neighbourhood, what kind of genus they are, when they were planted, and so on. To answer these questions, a exploratory data analysis and visualization on the vancouver tree dataset would be necessory.

In this report, I will explore the vancouver tree distribution by analyzing a subset of Vancouver Street Trees dataset (https://opendata.vancouver.ca/explore/dataset/street-trees/information/?disjunctive.species_name&disjunctive.common_name&disjunctive.height_range_id&disjunctive.on_street&disjunctive.neighbourhood_name). The data were obtained from The city of Vancouver's Open Data Portal and follows an Open Government Licence – Vancouver (https://opendata.vancouver.ca/pages/licence/). The data analysis and visualization of this dataset will give us more details of vancouver trees (subset is from https://raw.githubusercontent.com/UBC-MDS/data_viz_wrangled/main/data/Trees_data_sets/small_unique_vancouver.csv).

Question(s) of interests

By exploring this dataset, I am interested in answering the following questions:

• What is the number of trees planted in different year and month?
• Is there a relationship between tree diameter and height?
• What is the number of trees for different neighborhood and tree genus
• What is the top 5 tree genus distribution in different neighbourhood

For the final dashboard, I would like to present the tree details of specific/selected vancouver neighbourhood, such as the top 10 genus, tree diameter and height distribution, planted year, and street side name.

Analysis

Data imports

# Import libraries needed for EDA
import altair as alt
import pandas as pd
import numpy as np

# alt.data_transformers.enable('data_server')

# Load the dataset and parse the 'date_planted' as date datatype
van_tree_df = pd.read_csv('small_unique_vancouver.csv', parse_dates=['date_planted'])

# Take a look at all the columns of the dataset
van_tree_df.head()

	Unnamed: 0	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	...	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
0	10747	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	2000-02-23	28.5	EVEN	ACER	N	...	15	Y	21421	NORWAY MAPLE	4	0	NaN	N	49.252711	-123.106323
1	12573	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	...	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
2	29676	ROSS ST	ROSS ST	NIGRA	Sunset	NaT	12.0	ODD	PINUS	N	...	7	Y	154675	AUSTRIAN PINE	4	7800	NaN	N	49.213486	-123.083254
3	8856	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	...	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
4	21098	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	NaT	15.5	ODD	AESCULUS	Y	...	N	Y	74364	COMMON HORSECHESTNUT	4	5200	NaN	N	49.238514	-123.154958

5 rows × 21 columns

Data Description and analysis

First, let's take a look at the general information of this dataset and all the columns.

van_tree_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 21 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   Unnamed: 0          5000 non-null   int64         
 1   std_street          5000 non-null   object        
 2   on_street           5000 non-null   object        
 3   species_name        5000 non-null   object        
 4   neighbourhood_name  5000 non-null   object        
 5   date_planted        2363 non-null   datetime64[ns]
 6   diameter            5000 non-null   float64       
 7   street_side_name    5000 non-null   object        
 8   genus_name          5000 non-null   object        
 9   assigned            5000 non-null   object        
 10  civic_number        5000 non-null   int64         
 11  plant_area          4950 non-null   object        
 12  curb                5000 non-null   object        
 13  tree_id             5000 non-null   int64         
 14  common_name         5000 non-null   object        
 15  height_range_id     5000 non-null   int64         
 16  on_street_block     5000 non-null   int64         
 17  cultivar_name       2658 non-null   object        
 18  root_barrier        5000 non-null   object        
 19  latitude            5000 non-null   float64       
 20  longitude           5000 non-null   float64       
dtypes: datetime64[ns](1), float64(3), int64(5), object(12)
memory usage: 820.4+ KB

According to the information above, we are able to see that there are 5000 entries in total and 21 columns. The first column has no name. It is actually the index number of the original dataset (we are only analyzing the subset data). Therefore, this column will be dropped for further analysis.

# Drop the index column of original dataset
van_tree_df=van_tree_df.iloc[:,1:]
van_tree_df.head()

	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	civic_number	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
0	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	2000-02-23	28.5	EVEN	ACER	N	66	15	Y	21421	NORWAY MAPLE	4	0	NaN	N	49.252711	-123.106323
1	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	2323	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
2	ROSS ST	ROSS ST	NIGRA	Sunset	NaT	12.0	ODD	PINUS	N	7855	7	Y	154675	AUSTRIAN PINE	4	7800	NaN	N	49.213486	-123.083254
3	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	6938	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
4	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	NaT	15.5	ODD	AESCULUS	Y	5295	N	Y	74364	COMMON HORSECHESTNUT	4	5200	NaN	N	49.238514	-123.154958

Next, let us take a look at the details of the each colum's information from website https://opendata.vancouver.ca/explore/dataset/street-trees/information/?disjunctive.species_name&disjunctive.common_name&disjunctive.height_range_id&disjunctive.on_street&disjunctive.neighbourhood_name. The columns details are summarized in Table 1.

Table 1 Vancouver dataset column details

Column name	Datatype	Details
std_street	object	Street name of the site at which the tree is associated with
on_street	object	The name of the street at which the tree is physically located on
species_name	objec	Species name
neighbourhood_name	object	City's defined local area in which the tree is located.
date_planted	datetime	The date of planting in YYYYMMDD format.
diameter	float	DBH in inches (DBH stands for diameter of tree at breast height)
street_side_name	object	The street side which the tree is physically located on (Even, Odd or Median (Med))
genus_name	object	Genus name
assigned	object	Indicates whether the address is made up to associate the tree with a nearby lot (Y=Yes or N=No)
civic_number	int	Street address of the site at which the tree is associated with
plant_area	object	B = behind sidewalk, G = in tree grate, N = no sidewalk, C = cutout, a number indicates boulevard width in feet
curb	object	Curb presence (Y = Yes, N = No)
tree_id	int	Numerical ID
common_name	object	Common name
height_range_id	int	0-10 for every 10 feet (e.g., 0 = 0-10 ft, 1 = 10-20 ft, 2 = 20-30 ft, and10 = 100+ ft)
on_street_block	int	The street block at which the tree is physically located on
cultivar_name	object	Cultivar name
root_barrier	object	Root barrier installed (Y = Yes, N = No)
latitude	float	Location latitude
longitude	float	Location longitude

# Let us print out the summarized information for numeric columns
van_tree_df.describe()

	diameter	civic_number	tree_id	height_range_id	on_street_block	latitude	longitude
count	5000.000000	5000.000000	5000.000000	5000.00000	5000.000000	5000.000000	5000.000000
mean	12.340888	2975.707600	128682.584600	2.73440	2960.227000	49.247349	-123.107128
std	9.266600	2078.580429	75412.260406	1.56957	2086.861052	0.021251	0.049137
min	0.000000	2.000000	36.000000	0.00000	0.000000	49.202783	-123.220560
25%	4.000000	1300.500000	61321.500000	2.00000	1300.000000	49.230152	-123.144178
50%	10.000000	2639.000000	130130.500000	2.00000	2600.000000	49.247981	-123.105861
75%	18.000000	4123.000000	191332.000000	4.00000	4100.000000	49.263275	-123.063484
max	71.000000	9113.000000	270750.000000	9.00000	9100.000000	49.293930	-123.023311

This dataset has 7 numerical columns, including diameter, civic_number, tree_id, height_range_id, on_street_block, latitude and longitude. The rest of columns are categorical except date_planted is temporal.

# Let us print out the summarized information for categorical and temporal columns
van_tree_df.describe(exclude=[np.number],datetime_is_numeric=True)

	std_street	on_street	species_name	neighbourhood_name	date_planted	street_side_name	genus_name	assigned	plant_area	curb	common_name	cultivar_name	root_barrier
count	5000	5000	5000	5000	2363	5000	5000	5000	4950	5000	5000	2658	5000
unique	603	607	171	22	NaN	4	67	2	38	2	361	176	2
top	W 13TH AV	CAMBIE ST	SERRULATA	Renfrew-Collingwood	NaN	ODD	ACER	N	10	Y	KWANZAN FLOWERING CHERRY	KWANZAN	N
freq	52	49	463	384	NaN	2554	1218	4564	736	4593	383	383	4679
mean	NaN	NaN	NaN	NaN	2003-09-06 04:03:08.912399488	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
min	NaN	NaN	NaN	NaN	1989-10-31 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
25%	NaN	NaN	NaN	NaN	1997-11-06 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
50%	NaN	NaN	NaN	NaN	2003-02-12 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
75%	NaN	NaN	NaN	NaN	2009-11-17 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
max	NaN	NaN	NaN	NaN	2019-05-07 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

In this dataset, majority of the columns have 5000 entries, while date_planted, plant_area, cultivar_name has less entries, whose entries are 2363, 4950, 2658, respectively. Since date_planted and cultivar_name has only half of the entries of the total entries, I will keep the date_planted because it is one of the variables of my interest, but I will eliminate cultivar_name. Also, I will drop the NaN values of plant_area for further analysis. Last, I will drop some columns of no interest, including std_street, on_street, assigned, civic_number.

# Drop the columns of no interest and 'cultivar_name'
tree_df=van_tree_df.drop(columns=['std_street','on_street','assigned','civic_number','cultivar_name'])

# Drop the NaN rows in 'plant_area'
tree_df=tree_df.dropna(subset=['plant_area'])
tree_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4950 entries, 0 to 4999
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   species_name        4950 non-null   object        
 1   neighbourhood_name  4950 non-null   object        
 2   date_planted        2328 non-null   datetime64[ns]
 3   diameter            4950 non-null   float64       
 4   street_side_name    4950 non-null   object        
 5   genus_name          4950 non-null   object        
 6   plant_area          4950 non-null   object        
 7   curb                4950 non-null   object        
 8   tree_id             4950 non-null   int64         
 9   common_name         4950 non-null   object        
 10  height_range_id     4950 non-null   int64         
 11  on_street_block     4950 non-null   int64         
 12  root_barrier        4950 non-null   object        
 13  latitude            4950 non-null   float64       
 14  longitude           4950 non-null   float64       
dtypes: datetime64[ns](1), float64(3), int64(3), object(8)
memory usage: 618.8+ KB

Now the dataset is ready for visualization.

Exploratory Visualizations

Question 1: What is the number of trees planted in different year and month?

# Drop the NaN values of 'data_planted' for analysis
tree_date_df=tree_df.dropna(subset=['date_planted'])

# Create 'Year' and 'Month' columns from date_planted
tree_date_df=tree_date_df.assign(Year=tree_date_df['date_planted'].dt.year.astype(int))
tree_date_df=tree_date_df.assign(Month=tree_date_df['date_planted'].dt.month_name())
tree_date_df.head()

	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	root_barrier	latitude	longitude	Year	Month
0	PLATANOIDES	Riley Park	2000-02-23	28.5	EVEN	ACER	15	Y	21421	NORWAY MAPLE	4	0	N	49.252711	-123.106323	2000	February
1	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	7	Y	129645	CHANTICLEER PEAR	2	2300	N	49.256350	-123.158709	1992	February
3	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	N	49.220839	-123.036721	1999	November
5	PERSICA	West End	2012-04-05	3.0	EVEN	PARROTIA	C	Y	233622	VANESSA PERSIAN IRONWOOD	1	1100	N	49.281906	-123.133076	2012	April
7	OFFICINALIS	Kensington-Cedar Cottage	2001-04-02	3.0	EVEN	MAGNOLIA	N	Y	187792	CHINESE MAGNOLIA	2	3700	N	49.251127	-123.071912	2001	April

# Plot a bar chart to view the numbers of trees planted in different years and different month
year_chart=(alt.Chart(tree_date_df)
            .mark_bar()
            .encode(
                alt.X('Year:N', title='Tree planted year'),
                alt.Y('count()',title='Number of trees',axis=alt.Axis(grid=False)),
                alt.Color(value='green'),
                tooltip='count()')
            .properties(title='Number of trees planted in different year'))

month_order=['January','February','March','April','May','June','July','August','September','October','November','December']

month_chart=(alt.Chart(tree_date_df)
             .mark_bar().encode(
                 alt.X('Month:O', title='Tree planted month',sort=month_order),
                 alt.Y('count()',title='Number of trees', axis=alt.Axis(grid=False)),
                 alt.Color(value='pink'),
                 tooltip=('count()'))
             .properties(title='Number of trees planted in different month'))

date_chart=year_chart|month_chart
date_chart

Figure 1 Vancouver trees planted in different year and month

From Figure 1, it seems that from year 1989 to 1996, the number of trees planted is steadily increasing. Later from 1996 to 2013, the planted tree numbers fluctuates slightly and then starts dropping significantly from year 2013 to 2016. After that, the planted tree number starts slightly increasing again. For the number of planted trees for different month, Februry is the month with the most planted trees, while July and August have the minimum planted trees.

Question 2: Is there a relationship between tree diameter and height?

Usually, the tree with larger diameter is higher in height. Is this true for the vancouver tree dataset? Let's take a look at the relationship between the diameter and height_range_id.

# Creat a scatterplot for 'diameter' and 'height_range_id', set the count() for color channel
diameter_height_scatterplot=(alt.Chart(tree_df)
                             .mark_circle()
                             .encode(
                                 alt.X('height_range_id:Q', title='Tree height range id'),
                                 alt.Y('diameter:Q', title='Tree diameter (inch)'),
                                 alt.Color('count()', title='Number of trees'))
                             .properties(title='Relationship of tree diameter VS tree height range id'))

diameter_height_scatterplot

Figure 2 Relationship of diameter and height of Vancouver trees

From the scatterplot, we are able to see that the tree height and tree diameter has positive relationship, indicating that tree with larger diameter usually has higher height range id. However, this is a just overall trend, not apply to every single point. Therefore, I would like to do a boxplot to reveal more statistics and also add a line chart of mean diameter value to the scatterplot.

# Creat a boxplot for 'diameter' and 'height_range_id'
diameter_height_boxplot=(alt.Chart(tree_df)
                         .mark_boxplot()
                         .encode(
                             alt.X('height_range_id:Q', title='Tree height range id'),
                             alt.Y('diameter:Q', title='Tree diameter (inch)'))
                         .properties(title='Relationship of tree diameter VS tree height range id'))

# Creat a line chart using mean value of 'diameter' and add to scatterplot
diameter_height_lineplot=(alt.Chart(tree_df)
                          .mark_line(color='red')
                          .encode(
                              alt.X('height_range_id:Q', title='Tree height range id'),
                              alt.Y('mean(diameter):Q', title='')))

# Combine the scatterplot and line chart together, lay out with boxplot vertically
(diameter_height_lineplot + diameter_height_scatterplot)|diameter_height_boxplot

Figure 3 Scatterplot and boxplot of diameter and height of Vancouver tree

From Figure 3, it seems that the overall trend of relatishop between tree diameter and height range id (reflected by the mean and median values) is positive, except there is a slight decrease in diameter from tree height range id 8 to 9 (this could due to less datapoints).

Only knowing the relationship between the tree diameter and height is not enough for this report. I am more interested in the tree details of different neighbourhood. Therefore, I would like to present the tree diameter and height range id in rugplot. Add widget to select different neibourhood and genus, to see their tree diameter and height. Also, I am curious about that for specific/selected neighbourhood and genus, what is the number of trees of different street_side_name and when they were planted (tree planted year). Therefore, I created the following plot (Figure 4) to answer these questions and add it to the final dashboard.

neighbourhood = tree_df['neighbourhood_name'].unique()
dropdown_neighbourhood = alt.binding_select(name='Neighbourhood Name', options=neighbourhood)

genus = tree_df['genus_name'].unique()
dropdown_genus=alt.binding_select(name='Genus', options=genus)

select_neighbourhood_genus=alt.selection_single(fields=['neighbourhood_name','genus_name'], bind= {'neighbourhood_name': dropdown_neighbourhood, 'genus_name': dropdown_genus})

tree_diameter = (alt.Chart(tree_df)
                 .mark_tick()
                 .encode(
                     alt.X('diameter:Q', title='Diameter (inches)', scale=alt.Scale(domain=(0, 80))),
                     color=alt.condition(select_neighbourhood_genus, alt.value('blue'), alt.value('')))
                .add_selection(select_neighbourhood_genus))

tree_height = (alt.Chart(tree_df)
                 .mark_tick()
                 .encode(
                     alt.X('height_range_id:Q', title='Height range ID', scale=alt.Scale(domain=(0, 9))), 
                     color=alt.condition(select_neighbourhood_genus, alt.value('orange'), alt.value('')))
                .add_selection(select_neighbourhood_genus))

street_side_barplot = (alt.Chart(tree_df)
               .transform_filter(select_neighbourhood_genus)
               .mark_bar()
               .encode(
                   alt.X('street_side_name:N', title='Street side name'),
                   alt.Y('tree_count:Q', title='Number of trees'),
                   alt.Color(value='green'),
                   tooltip=[alt.Tooltip("tree_count:Q", title="Number of trees")])
               .transform_aggregate(
                   tree_count='count()', 
                   groupby=['street_side_name'])
                .add_selection(select_neighbourhood_genus))


year_barplot = (alt.Chart(tree_date_df)
               .transform_filter(select_neighbourhood_genus)
               .mark_bar()
               .encode(
                   alt.X('Year:N', title='Tree planted year'),
                   alt.Y('tree_count:Q', title='Number of trees'),
                   alt.Color(value='navy'),
                   tooltip=[alt.Tooltip("tree_count:Q", title="Number of trees")])
               .transform_aggregate(
                   tree_count='count()',
                   groupby=['Year'])
               .add_selection(select_neighbourhood_genus))

tree_detail_title = 'Tree details of different neighbourhood & genus'
tree_detail_plot = ((tree_diameter.properties(height=50) & tree_height & street_side_barplot.properties(height=100, width=200))| year_barplot.properties(width=500)).properties(title=alt.TitleParams(tree_detail_title, anchor='middle'))
tree_detail_plot

Figure 4 Tree details for different neighbourhood and genus

By selecting different neighbourhood and genus, we are able to see for specific genus in specific neighbourhood, what is its diameter and height range id, what is its street side number and how many of it has been planted over the year.

Question 3: What is the number of trees for different neighborhood and tree genus

I would like to find out the number of trees of different neighborhood and tree genus by making the following barplots.

categorical_columns=['neighbourhood_name','genus_name']

repeat_plot = (alt.Chart(tree_df)
               .mark_bar()
               .encode(
                   alt.X('count()', title='Number of trees'), 
                   alt.Y(alt.repeat(), type='nominal', title='',sort='-x'), 
                   alt.Color(value='navy'), 
                   tooltip=alt.Tooltip("count()", title="Number of trees"))
               .properties(width=200, height=800)
               .repeat(categorical_columns))

repeat_plot.properties(title=alt.TitleParams('Number of trees of different neighbourhood and different genus', anchor='middle'))

Figure 5 Number of trees of different neighbourhood and genus

The top 5 neighbourhood with most tree number are Kensington_Cedar Cottage, Renfrew_Collingwood, Hastings_Sunrise, Dunbar_Southlands, Sunset. The top 5 tree genus planted are ACER, PRUNUS, TILIA, FRAXINUS, QUERCUS. How these top 5 genus trees distributed in different neighbourhood? Let us analyze this in the next question.

Question 4: What is the top 5 tree genus distribution in different neighbourhood

# Filter the data only including top 5 tree genus
filtered_genus_df=tree_df.query("genus_name == ['ACER', 'PRUNUS', 'TILIA', 'FRAXINUS', 'QUERCUS']")
filtered_genus_df

# Plot the bar chart and add tooltip for tree count
neighbourhood_genus_plot=(alt.Chart(filtered_genus_df)
                          .mark_bar()
                          .encode(
                              alt.X('count()', title='Number of trees'),
                              alt.Y('genus_name:N', title=''),
                              alt.Color('genus_name', scale=alt.Scale(scheme='set3')),
                              tooltip='count()').properties(width = 200).facet('neighbourhood_name', columns=5))

neighbourhood_genus_plot.properties(title=alt.TitleParams('Number of trees of Top 5 genus in different neighbourhood', anchor='middle'))

Figure 6 Number of trees of top 5 genus in different neighbourhood

From Figure 6, we are able to see that among the 5 genus, ACER and PRUNUS are the top 2 genus planted for almost all the neighbourhoods (except Downtown). The number of trees of these 5 genus is different for different neighbourhood.

Even though Figure 6 can give us a lot of information of top 5 tree gunus of different neighbourhood but it is not flexible. What if I want to know the top 10 genus? What if I am only interested in a specific/selected neighbourhood, such as West End? In this case, there is no need to show data for other neighbourhoods. Therefore, I have imporved the plot by applying selection features as follows.

click_1 = alt.selection_single(fields=['neighbourhood_name'])

neighbourhood_barplot = (alt.Chart(tree_df)
                         .mark_bar().encode(
                             alt.X('neighbourhood_name:N', title='Neighbourhood name'),
                             alt.Y('count()',title='Number of Trees'),
                             alt.Color('neighbourhood_name:N', scale=alt.Scale(scheme='set3'), legend=None),
                             opacity=alt.condition(click_1, alt.value(1), alt.value(0.1)),
                         tooltip='count()')
                         .add_selection(click_1))

genus_barplot = (alt.Chart(tree_df)
               .transform_filter(click_1)
               .mark_bar()
               .encode(
                   alt.X('tree_count:Q', title='Number of Trees', scale=alt.Scale(domain=(0, 1300))),
                   alt.Y('genus_name:N', sort='-x', title='Genus name'),
                   alt.Color(value='purple'),
                   tooltip=[alt.Tooltip("tree_count:Q", title="Number of trees")])
               .transform_aggregate(
                   tree_count='count()',
                   groupby=['genus_name'])
               .transform_window(
                   rank='rank(tree_count)',
                   sort=[alt.SortField('tree_count', order='descending')])
               .transform_filter(alt.datum.rank <=10)
               .add_selection(click_1))

neighbourhood_genus_title = 'Number of trees of different genus for different neighbourhood'
neighbourhood_genus_plot= (neighbourhood_barplot.properties(height=200) | genus_barplot).properties(title = alt.TitleParams(neighbourhood_genus_title, anchor='middle'))

neighbourhood_genus_plot

Figure 7 Number of trees of top 10 genus for different neighbourhood

From Figure 7, we are able to find the top 10 genus (in descending order) of one specific/selected neighbourhood by clicking on the barchart. Also, I have added interactive feature to get the number of trees of different genus. This is a more convenient and efficient way to get the required information. We will include the Figure 7 in the final dashboard.

Discussion

The main purpose of this dataset analysis and visualization is to explore detailed information about vancouver trees. From the previous analysis, there are some interesting points I have found to answer the questions mentioned at beginning of this report.

First, it seems that the number of trees planted in different year and month are quite different (Figure 1). The number of trees planted steadily increases from year 1989 to 1996. Later from 1996 to 2013, the planted tree number fluctuates slightly and then starts dropping significantly from year 2013 to 2016. After that, the planted tree number starts slightly increasing again. Most trees were planted in Februry, while least were planted in July and August. It is recommended to plant trees in the rain season to ensure the survival of saplings especially in the first few months after they are planted (https://essc.org.ph/content/view/132/). This is maybe the reason we see the number of trees starts increasing from October, then peaks in February, and slows down from May, which follows the vancouver average precipitation trend (https://weather-and-climate.com/average-monthly-precipitation-Rainfall,vancouver,Canada).

From common sense, trees with larger diameters usually has higher height (positive correlation). This is supported by our scatterplot and boxplot (Figure 3). However, we do observe a slight decrease in diameter from tree height range id 8 to 9. This could be due to less datapoints. Another reason could be the way to present the tree height (based on range id, not actual height). To improve this or get more confirmed result, collecting more data and present data of real tree height (measured in inch) would be helpful.

From Figure 5 and 6, we are able to see that different vancouver neightbourhood has different number of trees and different genus. The top 5 neighbourhood with most tree number are Kensington_Cedar Cottage, Renfrew_Collingwood, Hastings_Sunrise, Dunbar_Southlands, Sunset. The top 5 tree genus planted are ACER, PRUNUS, TILIA, FRAXINUS, QUERCUS. By clicking the interactive plot (Figure 7), we are able to look into more details of top ten genus of different neighbourhood.

For the final interactive dashboard, the goal is to build a tool for goveronment or people who care about understanding their community/neighbourhood tree distribution/details. By using this dashboard, more detailed information about trees, such as genus, diameter & height, tree planted year and street side name, will be presented in an more convenient and efficient way.

This data visualization helps me answer all my questions and the results meet my expectation. Other information I would like to explore is to dig deeper into the tree species and common name of different neighouborhood. In addition, the current dataset just includes the tree numbers, not population of neighbourhood. Neighbourhood with more population could plant more trees. If the dataset could have included the population of the neighbourhood, it would be helpful to understand the number of trees/person, which will give us a better idea about how the community has done regarding the tree planting.

Interactive Dashboard

Now, I am ready to make the final dashboard. For the dashboard, I would like to combine Figure 4 and Figure 7 together. So it shows all the information about the tree details for different vancouver neighbourhood. The dashboard is coded as follows and show in Figure 9.

dashboard_title =alt.TitleParams(
    'Vancouver trees dataset analysis and visualization', 
    subtitle = ['What is the tree details for different neighbourhood?'],
    fontSize=30, subtitleFontSize = 20, align ='center', anchor='middle')

dashboard_plot = (neighbourhood_genus_plot & tree_detail_plot).properties(title=dashboard_title)
dashboard_plot

Figure 9 Dashboard of Vancouver Tree Dataset analysis and visualization

Reference

• https://vancouver.ca/news-calendar/our-city.aspx
• https://opendata.vancouver.ca/explore/dataset/street-trees/information/?disjunctive.species_name&disjunctive.common_name&disjunctive.height_range_id&disjunctive.on_street&disjunctive.neighbourhood_name
• https://raw.githubusercontent.com/UBC-MDS/data_viz_wrangled/main/data/Trees_data_sets/small_unique_vancouver.csv
• https://opendata.vancouver.ca/pages/licence/
• https://essc.org.ph/content/view/132/
• https://weather-and-climate.com/average-monthly-precipitation-Rainfall,vancouver,Canada